RESUMO
The cyclic GMP-AMP synthase-stimulator of the interferon genes (cGAS-STING) pathway is essential in inflammation-driven tumor occurrence and progression. However, the prognostic roles and immune functions of cGAS-STING pathway-related genes in patients with prostate adenocarcinoma (PRAD) remain unclear. cGAS-STING pathway-related genes were obtained from the gene set enrichment analysis (GSEA) website. Univariate Cox regression analysis was performed to screen the prognosis-related hub genes in the cancer genome atlas (TCGA) and GSE116918 datasets. Unsupervised clustering analysis was performed to identify different clusters. The least absolute shrinkage and selection operator and multivariate Cox regression analyses were applied to develop a prognostic risk model. The prognostic values and predictive performance of risk signature were assessed by the Kaplan-Meier curve and receiver operating characteristic curve. The IMvigor210 cohort was used to investigate the potential values of the risk score in immunotherapeutic responses. Two clusters were identified based on the expression matrix of 12 prognosis-related genes. Specifically, better overall survival was observed in cluster 2 than cluster 1 in both datasets. Inflammation-related pathway enrichment and immune cell infiltration levels were altered between 2 clusters. Moreover, 6 genes (CASP8, GRK6, IL3RA, PLCB1, TBKBP1, and TNFSF10) were identified to generate a cGAS-STING pathway-related signature (CPRS). Survival analysis showed that patients in the high-risk group showed a more dismal survival than those in the low-risk group in TCGA and GSE116918 datasets. Notably, the CPRS can differentiate responsive patients from non-responsive individuals treated with PD-L1 blockades in an independent cohort. In addition, higher CPRS was associated with a more favorable prognosis. The proposed risk model was developed based on 6 cGAS-STING pathway related-genes, which can be used as a promising predictor for patient survival and immunotherapeutic responses in PRAD, contributing to treatment strategy-related decision-making.
